论文标题
未签名的距离字段作为神经场景完成的准确3D场景表示
Unsigned Distance Field as an Accurate 3D Scene Representation for Neural Scene Completion
论文作者
论文摘要
场景完成是从场景的部分扫描中完成缺失几何形状的任务。大多数以前的方法使用在3D网格上计算的截短签名距离函数（T-SDF）计算出隐式表示，该表示是神经网络的输入。截断降低，但不会消除SDF符号为开放表面引入的边框错误。作为替代方案，我们提出了一个未签名的距离函数（UDF）作为场景完成神经网络的输入表示。所提出的UDF简单且有效作为几何表示，并且可以在任何点云上计算。与通常的签名距离功能相反，我们的UDF不需要正常的计算。为了获得明确的几何形状，我们提出了一种从稀疏网格上离散的UDF值提取点云的方法。我们比较了使用RGB-D和LIDAR传感器收集的室内和室外点云上的场景完成任务的不同SDF和UDF，并使用建议的UDF功能显示了改进的完成。
Scene Completion is the task of completing missing geometry from a partial scan of a scene. Most previous methods compute an implicit representation from range data using a Truncated Signed Distance Function (T-SDF) computed on a 3D grid as input to neural networks. The truncation decreases but does not remove the border errors introduced by the sign of SDF for open surfaces. As an alternative, we present an Unsigned Distance Function (UDF) as an input representation to scene completion neural networks. The proposed UDF is simple, and efficient as a geometry representation, and can be computed on any point cloud. In contrast to usual Signed Distance Functions, our UDF does not require normal computation. To obtain the explicit geometry, we present a method for extracting a point cloud from discretized UDF values on a sparse grid. We compare different SDFs and UDFs for the scene completion task on indoor and outdoor point clouds collected using RGB-D and LiDAR sensors and show improved completion using the proposed UDF function.
